To Get Entry-Level Jobs

Top Data Science Skills

Programming Language

It is a no-brainer to discuss Python vs R, as data science is not only about the tools. You can use any tool as long as you can get the desired output. Learn a programming language of your choice and start analyzing data by getting familiar with popular libraries. A data scientist is a problem solver, not only a Python programmer.

Data Analysis and Machine Learning

Learn data analysis and machine learning processes from any free or paid course to strengthen your basics. You cannot learn everything in machine learning at once, but go one full stretch to understand the concepts. Eventually, you can come back and learn in-depth in any subdomain of machine learning such as computer vision or NLP or others.

Inferential Statistics and Mathematics

Statistics and mathematics are the foundation of data science. However, beginners mostly focus on programming languages, ignoring the foundation of data science–statistics and mathematics. This does not mean that you should ignore programming languages. But, at the same time, you should have in-depth knowledge of statistics and mathematics.

Data Intuition

Often data intuition is overlooked by beginners because they believe that it is only vital while handling large data. Although it is an essential skill while managing a colossal amount of information, the ability to make out the most from less data is equally important. Given a data (small or big), you should be able to quickly think of approaches that can be best suited to bring business value.


Storytelling is one of the most important data science skills. If you fail to communicate with business leaders about your analysis in straightforward or simple terms, your machine learning models might never go into production. Such instances will not only negatively afflict the organizations but also your reputation as data scientists. Storytelling is not only for internal communication. In organizations, you will have to talk to clients who might not be aware of data science terms.

Proficiency In Working With Unstructured Data

As per a report, 80 to 90 percent of data in organizations is unstructured. Over the years, organizations were focusing on tabular data, but now businesses have realized the essence of unstructured data. Therefore, being proficient in handling unstructured data can differentiate you from other applicants and help you land a data science job.

Software Development Understanding

Most of the business problems can be solved with simple supervised machine learning models. You must try to avoid using fancy deep learning techniques until necessary. This will help software developers to productize their machine learning models. Therefore, never try to bring in deep learning models to demonstrate your data science skills in interviews when the simple machine learning models can, more or less, deliver the same results.



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